Generally, computer systems generate logs for troubleshooting that can be good indicators of brewing problems. These logs contain valuable information associated with the run-time behavior of the system (e.g., whether transactions are going through successfully). Unfortunately, the large volume of the logs makes sifting through the data manually unfeasible and the complexity of the distributed systems lowers the efficiency of any manual diagnosis. Methods and applications currently exist that can mine these logs and subsequently create a control flow graph (CFG) that can be used to identify anomalous system behavior. However, these conventional methods all contain a variety of limitations that prevent them from accurately reporting all possible deviations in execution flow. To date, users have lacked a sufficiently efficient method for scalable high precision mining of the CFG from logs.